Biometric identification and verification

ABSTRACT

In real biometric systems, false match rates and false non-match rates of 0% do not exist. There is always some probability that a purported match is false, and that a genuine match is not identified. The performance of biometric systems is often expressed in part in terms of their false match rate and false non-match rate, with the equal error rate being when the two are equal. There is a tradeoff between the FMR and FNMR in biometric systems which can be adjusted by changing a matching threshold. This matching threshold can be automatically, dynamically and/or user adjusted so that a biometric system of interest can achieve a desired FMR and FNMR.

RELATED APPLICATION DATA

This application claims the benefit of and priority under 35 U.S.C.§119(e) to U.S. Patent Application Nos. 61/125,577, filed Apr. 25, 2008,entitled “BIOMETRIC IDENTIFICATION METHOD AND SYSTEM,” and 61/134,170,filed Jul. 7, 2008, entitled “BIOMETRIC VERIFICATION METHOD AND SYSTEM,”both of which are incorporated herein by reference in their entirety.

BACKGROUND Field of the Invention

This invention generally relates to biometrics. More specifically, anexemplary embodiment of the invention relates to biometricidentification. Another exemplary embodiment of the invention relates tobiometric verification.

SUMMARY

Biometric systems are used for such things as access control,fingerprint databases, facial recognition and retinal identification,and are in general used to assist with the identification of anindividual. A biometric identification solution operates bycharacterizing a biometric sample (probe) such as a fingerprint, andthen using mathematical algorithms to identify the most similar samplesin a database (gallery). If one or more samples in the gallery arederived from the same person and source as the probe sample, then thematching algorithm will attempt to identify them as such with a highsimilarity score.

A theoretical perfect biometric algorithm would always identify with100% confidence that the samples do in fact match (0% false non-matchrate). That is, the samples are derived from the same source, albeit atdifferent times. Similarly, if there does not exist a sample in thedatabase derived from the same source, then a theoretical perfectbiometric solution would always identify with 100% confidence that asample matching the probe sample does not exist in the gallery (0% falsematch rate).

However, in real biometric systems, false match rates and falsenon-match rates of 0% do not exist. There is always some probabilitythat a purported match is false, and that a genuine match is notidentified.

The performance of biometric systems has often been expressed in part interms of False Match Rate (FMR) and False Non-Match Rate (FNMR), withthe Equal Error Rate (EER) being the rate at which the FNMR and the FMRare equal.

FIG. 1 illustrates an example of match results exhibited by a truebiometric identification system. Imposter samples are those known to bederived from a different source than that of the probe. Genuine samplesare those known to be derived from the same source as that of the probe.

FIG. 2 illustrates that by setting a threshold, the system is able toachieve a desired tradeoff between the FNMR and FMR as shown in thezoomed in circular portion of FIG. 1. FIG. 3 illustrates the effect ofincreasing and decreasing the threshold. There is a tradeoff between FMRand FNMR and therefore there is a point (and a threshold) at which theFMR and FNMR are equal. This rate is the equal error rate as mentionedabove.

Biometric verification solutions operate by characterizing a livebiometric sample, such as a fingerprint, and then using mathematicalalgorithms to quantify the similarity of the live sample to a singleexisting sample known to be derived from the individual in question. Ifthis similarity between the two samples is sufficiently high, that is,exceeds some previously specified threshold, then it can be said thatthe identity of the individual has been biometrically verified.Biometric verification might also be called “one-to-one matching” andhas a different application from “biometric identification,” or“one-to-many matching,” which in contrast measures the similaritybetween the live biometric sample (probe) and a gallery of samples in anattempt to identify which sample is most similar and therefore mostlikely to be derived from the same individual.

A theoretical perfect biometric verification algorithm would alwaysidentify with 100% confidence that the samples do in fact match (0%false non-match rate). That is, the samples are derived from the samesource, albeit at different times. Similarly, such an algorithm wouldnever indicate that samples match if they are derived from differentsources (0% false match rate).

In real biometric systems, false match rates and false non-match ratesof 0% do not exist. There is always some probability that a purportedmatch is false, and that a genuine match is not identified. Theperformance of biometric systems is often expressed in part in terms oftheir false match rate and false non-match rate as discussed above, withthe equal error rate being when the two are equal. Similar to biometricidentification, there is a tradeoff between the FMR and FNMR withbiometric verification which can be adjusted by changing a matchingthreshold. More specifically, and as illustrated in FIG. 12, an exampleof match results exhibited by a true biometric verification system isshown. Imposter samples are those known to be derived from a differentsource than that of the probe. Genuine samples are those known to bederived from the same source as that of the probe. FIG. 13 is anenlarged portion of the circled portion of FIG. 12 and illustrates howthe setting of a threshold enables the system to yield a desiredtradeoff between the FNMR and FMR.

In FIG. 14, the effect of increasing and decreasing the threshold isillustrated. There is a tradeoff between FMR and FNMR. There is also apoint (and a threshold) at which the FMR and the FNMR are equal—as withbiometric identification discussed above, this rate is the equal errorrate.

Exemplary aspects of the invention are thus directed toward biometricidentification. Additional aspects of the invention are directed towardgenerating a database of match scores between pluralities of impostersampled pairs in a gallery.

Still further aspects of the invention are directed toward determining afalse match rate associated with a threshold, wherein the threshold isdetermined by cumulative histogram data table recording, for eachpossible match score value, the number of match scores observed greaterthan that value divided by the total number of samples.

Even further aspects of the invention are directed toward selecting afalse match rate and determining a threshold that will result in thedesired false match rate, wherein the threshold is determined by acumulative histogram data table recording, for each possible match scorevalue, the number of match scores observed greater than that valuedivided by the total number of samples.

Even further aspects of the invention relate to generating a database ofmatch scores between pluralities of genuine sample pairs in a gallery,wherein the genuine sample pairs are derived from two samples, each fromthe same source.

Even further aspects of the invention are directed toward determining afalse non-match rate associated with a threshold, wherein the thresholdis determined by a cumulative histogram data table recording, for eachpossible match score value, the number of match scores observed greaterthan that value divided by the total number of samples.

Still further aspects of the invention relate to selecting a falsenon-match rate and determining a threshold that will result in thedesired false non-match rate, wherein the threshold is determined by acumulative histogram data table recording, for each possible match scorevalue, the number of match scores observed greater than that valuedivided by the total number of samples.

Additional aspects of the invention relate to biometric verification.

Further aspects relate to generating a database of match scores betweenpluralities of imposter sample pairs in a gallery, wherein impostersample pairs are derived from two samples, each from a different source.

Even further aspects of the invention relate to determining a falsematch rate associated with a threshold, wherein the threshold isdetermined by a cumulative histogram data table recording, for eachpossible match score value, the number of match scores observed greaterthan that value divided by the total number of samples.

Even further aspects of the invention relate to a biometric verificationsystem selecting a false match rate then determining a threshold thatwill result in the desired false match rate, wherein the threshold isdetermined by a cumulative histogram data table recording, for eachpossible match score value, the number of match scores observed greaterthan that value divided by the total number of samples.

These and other features and advantages of this invention are describedin, or are apparent from, the following detailed description of theexemplary embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

The exemplary embodiments of the invention will be described in detail,with reference to the following figures, wherein:

FIG. 1 illustrates an exemplary graph of match results according to thisinvention;

FIG. 2 illustrates the relationship between biometric identificationFNMR and FMR according to this invention;

FIG. 3 illustrates the effect of increasing and decreasing the thresholdillustrated in FIG. 2 according to this invention;

FIG. 4 illustrates an exemplary biometric identification andverification system according to this invention;

FIG. 5 illustrates an exemplary method for using imposter match scoredata to automate derivation of a match threshold score that results inthe desired biometric identification system false match rate and falsenon-match rate according to this invention;

FIG. 6 illustrates an example of the determination of the false matchrate for the biometric identification system according to thisinvention;

FIG. 7 illustrates an exemplary method for using genuine match scoredata to automate derivation of a match threshold score that results inthe desired biometric identification system false match rate and falsenon-match rate according to this invention;

FIG. 8 illustrates an exemplary determination of a false match rate forthe biometric identification system according to this invention;

FIG. 9 illustrates exemplary false match confidence score determinationaccording to this invention;

FIG. 10 illustrates an exemplary method for applying the techniquesdisclosed herein to a multi-sample environment according to thisinvention;

FIG. 11 illustrates an exemplary method for applying the techniquesdisclosed herein to a multi-modal environment according to thisinvention;

FIG. 12 illustrates an example of match results exhibited by a truebiometric verification system according to this invention;

FIG. 13 illustrates the setting of a biometric verification systemthreshold according to this invention;

FIG. 14 illustrates the equal error rate according for the biometricverification system according to this invention;

FIG. 15 illustrates an exemplary method for using previous genuine andimposter match score data to automate derivation of an individualverification match threshold score according to this invention;

FIG. 16 illustrates a comparison of a number of scores to match scoresaccording to this invention;

FIG. 17 illustrates the effects of lowering the threshold according tothis invention; and

FIG. 18 illustrates the effects of setting a higher threshold accordingto this invention.

DETAILED DESCRIPTION

The exemplary systems and methods of this invention will be described inrelation to biometric identification and verification. However, to avoidunnecessarily obscuring the present invention, the following descriptionomits well-known structures and devices that may be shown in blockdiagram form or are generally known or otherwise summarized. Forpurposes of explanation, numerous specific details are set forth inorder to provide a thorough understanding of the present invention. Itshould however be appreciated that the present invention may bepracticed in a variety of ways beyond the specific detail set forthherein.

Furthermore, while the exemplary embodiments illustrated herein show thevarious components of the system collocated, it should be appreciatedthat the various components of the system can be located at distantportions of a distributed network, such as a LAN and/or the internet orwithin a dedicated system. Thus, it should be appreciated that thecomponents of this system can be combined into one or more devices orcollocated on a particular node of a distributed network, such as acommunications network. It will be appreciated from the followingdescription, and for reasons of computational efficiency, that thecomponents of the system can be arranged at any location within adistributed network without affecting the operation of the system.Furthermore, it should be appreciated that the various links connectingthe elements can be wired or wireless links, or any combination thereof,or any other known or later developed element(s) that is capable ofsupplying and/or communicating data to and from the connected elements.These wired or wireless links can also be secure links and may becapable of communicating encrypted information.

The term module as used herein can refer to any known or later developedhardware, software, or combination of hardware and software that iscapable of performing the functionality associated with that element.Also, while the invention is described in terms of exemplaryembodiments, it should be appreciated that individual aspects of theinvention can be separately claimed. While the embodiments discussedherein will be directed toward fingerprint biometrics, it should also beappreciated that the systems and methods will work equally well for anytype of biometric information, such as digital information representinga physical feature of an animal or human, that includes any type ofbiometric including, but not limited to, images of fingers, hands, feetor any portion thereof, retinal information, iris information and thelike.

The exemplary systems and methods of this invention will be described inrelation to biometric identification and verification. However, to avoidunnecessarily obscuring the present invention, the following descriptionomits well-known structures and devices that may be shown in blockdiagram form, are generally known or otherwise summarized. For purposesof explanation, numerous specific details are set forth in order toprovide a thorough understanding of the present invention. It shouldhowever be appreciated that the present invention may be practiced in avariety of ways beyond the specific detail set forth herein.

FIG. 4 illustrates an exemplary biometric identification andverification system 100. In addition to well known componentry, thesystem includes a match score module 110, a histogram module 120, athreshold module 130, a confidence level module 140, an I/O controller150, processor 160, memory/storage 170, alert module 180, frequencymodule 190, enrollment database 192, gallery 194, user interface module196 and one or more databases 198.

The biometric identification and verification system 100 receivesbiometric samples from one or more of an access control system 210,fingerprint scanner 220, facial, retinal, iris and other biometricscanning/input devices 230. The biometric identification andverification system 100 can also optionally be connected, via link 5, toone or more networks 10 that can link, for example, additional galleries260. In addition, the biometric identification and verification system100 can be attached to one or more output devices 250, such as adisplay, and input devices 240, such as a keyboard, mouse or the like.

Typically, a biometric sample is taken from a user and compared to knownsamples in one or more galleries. Upon the completion of thiscomparison, the sample from the user is discarded. However, inaccordance with an exemplary embodiment of this invention, one or morebiometric samples are stored in, for example, gallery 194 or gallery 260and this historical data used in accordance with the techniquesdisclosed herein.

Biometric Identification

More specifically, and in accordance with a first exemplary embodiment,imposter match score data is used to automate derivation of a matchthreshold score that results in the desired biometric identificationsystem false match rate and false non-match rate.

There exists a problem in biometric systems where selecting a matchthreshold for a given biometric system such that the desired FMR/FNMRlevels can be targeted, is a manual, and imprecise process that does notenable the user to consider data from the system in question andautomate the threshold setting to optimize undesired error rates. Thisproblem can be addressed using imposter match score data.

More specifically, and in cooperation with the match score module 110and enrollment database 192, a database of match scores is generatedbetween all unique imposter sample pairs in a gallery, such as gallery194 or 260. The imposter sample pairs are derived from two samples, eachfrom a different source. Next, the histogram module 120 creates acumulative histogram data table recording for each possible match scoreoutcome the number of match scores observed greater than that valuedivided by the total number of samples. This value is the “false matchrate” associated with each possible threshold used in the system. A userinterface module 196, in cooperation with the output device 250 andinput device 240 provides to the user a user interface via which theycan select a desired false match rate and apply the threshold that willresult in that desired false match rate. This threshold is then appliedwith the cooperation of the threshold module 130.

As an example, a gallery database has 10,000 samples derived from 10,000different individuals. The number of unique match scores generated inthis manner is equal to n (n−1)/2 where n is the number of samples inthe database. The number of unique match scores is therefore 49,995,000.

A user, interfacing with the user interface module 196, can thenindicate that they desire the system to perform with a false match rateof, for example, 1 in 10,000. This aligns with the total number of matchscores above a threshold of 5,000. In the cumulative histogram datatable described above, there is a match score threshold that results ina total of 5,000 scores above it. The threshold module 130 could thenapply this threshold to the system. Once applied, and in cooperationwith the alert module 180, an alert could optionally be generated andforwarded to a destination, such as output device 250, to alert the userto the resulting FMR with the threshold in use based on actual resultsusing the gallery samples in use. Examples of alerts include text-basedalerts such as an email, as well as audible or visual alerts displayedin cooperation with the display.

In accordance with a second exemplary embodiment, the genuine matchscore data is used to automate derivation of a match threshold scorethat results in the desired system false match rate and false non-matchrate. More specifically, this problem can be addressed using any matchscore data. More specifically, match score module 110, in cooperationwith the enrollment database 192 and one or more galleries, generates adatabase of match scores between all unique genuine sample pairs in agallery. The genuine sample pairs are derived from two samples, eachfrom the same source.

Next, the histogram module 120 creates a cumulative histogram data tablerecording for each possible match score outcome the number of matchscores observed greater than that value divided by the total number ofsamples. This value is the false non-match rate associated with eachpossible threshold used in the system. A user, in cooperation with theuser interface module 196, can then select a desired false non-matchrate and apply the threshold with the cooperation of the thresholdmodule 130 that will result in the desired false non-match rate.

For example, a gallery database has 10,000 samples with two derived fromeach of 5,000 different individuals. The number of unique genuine matchscores generated in this manner is 5,000. A user can then specify thatthey desire the system to perform with a false non-match rate of 1 in1,000. This aligns with a total number of match scores below the desiredthreshold of five. In the cumulative histogram data table described,there is a match score threshold that results in a total of five scoresbelow it. The threshold module 130 could then apply this threshold tothe system. As discussed above, the alert module 180 can also beprogrammed to alert the user to the resulting FNMR with the threshold inuse based on actual results using the gallery samples in use.

As illustrated in FIG. 6, there are 49,995,000 match scores betweenimposter samples. With a threshold being defined as “T”, 5,000 matchscores are above that threshold. In FIG. 8, an illustration of thedetermination of false non-match rate is shown, with five falsenon-match results, 5,000 genuine match scores and again, a threshold of“T.”

In accordance with a third exemplary embodiment, imposter match behaviorof gallery samples is used to add a false match probability score to amatch score in order to make better match/no match decisions. Moreparticularly, this exemplary embodiment is based upon the premise thatthe “matchability” of both probe and gallery samples is useful inachieving higher confidence in higher match score results from a pair ofsamples. For example, some samples may illustrate a higher tendency tomatch than others. Some may have such a high tendency to match that theyincrease the false match rate of the biometric system. For example,consider the theoretical circumstance of a biometric sample in a gallerythat exhibits the highest match score to every submitted probe sample.This would yield a false match rate of 100%. By assessing thematchability of gallery samples, which will be identified as a falsematch probability score, this data can be incorporated into the matchresults and thereby reduces the occurrence of false matches and falsenon-matches.

This false match probability score can be generated in cooperation withthe confidence level module 140, in cooperation with one or more of I/Ocontroller 150, processor 160, memory 170 and the various databases andgalleries disclosed herein. For example, consider the scenario of aone-to-many search where a single probe sample P1 is compared against agallery G1 of 10,000 samples G(x). The matching algorithm indicates,when compared to probe sample P1, that three gallery samples G(a), G(b)and G(c) yield match scores equal to or greater than the operating matchthreshold of T=85. It may be useful to know what the probability is thata match score result is actually an imposter match, i.e., a false matchresult.

The probability of a high-scoring match being false is assessed by: Thefrequency module 190, for each of the three probe/gallery sample pairsP1:G(a), P1:G(b), and P1:G(c), counting the following:

(a) FMO(P1:G(x))—within the corpus of false match score data yieldedfrom the comparison between the probe sample P1 and all gallery G(x)samples, the observed number of occurrences of the false match scorebeing greater than the match score result between the probe P1 andgallery samples G(a), G(b), and G(c).

(b) FMO(G(x):G1)—within the corpus of false match score data yieldedfrom the comparison between gallery samples G(a), G(b), and G(c) and therest of the gallery G1, the observed number of occurrences of falsematch scores above the match score result of the pair

(c) FMP(P1:G(x))—probability of false match resulting from either P1 orG(x)

Note that regarding probability and determination of FMP:

P(A and B)=n(A and B)/n(S) where n(S) is the total number of possibleoutcomes

P(A or B)=P(A)+P(B)−P(A and B)

TABLE 1 results of 1: many match of a single probe sample G(a) G(b) G(c)Match score algorithm result 95 90 85 M(P1:G(x)) FMO(P1:G(x)) Falsematch 0 1 2 count occurrences count FMO(P1:G(x)) False match 0.00% 0.01%0.02% percentage occurrences percentage FMO(G(x):G1) False match 12 2 3count occurrences count FMO(G(x):G1) False match 0.12% 0.02% 0.03%percentage occurrences percentage FMP(P1:G(x)) False match 0.1200%0.0300% 0.0500% probability

From this result, it is evident that G(b) could be a more reliable matchto P1 than G(a), even though its match score is lower, because theprobability of a false match is less.

These techniques can also be applied to a multi-sample environment. Forexample, this technique can be used in order to apply appropriateweighting to a multi-sample probe.

For example, with a gallery G1 of 10,000 pairs of samples G(x) and aprobe P1 each including 2 samples from the same source, such as left andright fingerprint samples:

A, B, C, D, and E represent the genuine match candidates in gallery G1with the five highest comparison score results between probe samplepairs and gallery sample pairs as generated for each pair by thealgorithm.

M(P11:G(X)1) and M(P1 r:G(X)r) represent each match score betweenrespective left and right samples from probe set P1 and samples G(X).

FMO(P11:G(X)1) represents the occurrences of false match scores abovethe given match score of the pair between each probe sample set and theentire gallery.

FMO(G(X)1:G11) represents the occurrences of false match scores abovethe given match score of the pair between each gallery sample set andthe entire gallery.

FMO(P11:G11) and FMO(P1 r:G1 r) represents the sum of these false matchoccurrences observed with the probe sample set and gallery sample setfor left and right samples respectively.

FMP(P1:G1) represents the probability of a false match result of thegiven probe/gallery set.

Note that regarding probability and calculation of FMP:

P(A and B)=n(A and B)/n(S) where n(S) is the total number of possibleoutcomes

P(A or B)=P(A)+P(B)−P(A and B)

TABLE 2 results of 1: many match of a two-sample probe and gallery of10,000 sample sets sample G(A) G(B) G(C) G(D) G(E) M(P1l:G(X)l) matchscore left 95 90 85 80 75 M(P1r:G(X)r) match score right 90 99 92 80 94FMO(P1l:G(X)l) false match left 0 1 2 3 5 occurrences FMO(P1r:G(X)r)false match right 4 0 3 6 1 occurrences FMO(G(X)l:G1l) false match left8 4 16 31 25 occurrences FMO(G(X)r:G1r) false match right 36 8 14 27 19occurrences FMO(P1l:G1r)l false match left 8 5 18 34 30 occurrences sumFMO(P1r:G1r) false match right 40 8 17 33 20 occurrences sum FMP (P1:G1)False match 0.4797% 0.1300% 0.3497% 0.6689% 0.4994% probability

One problem with the match score is illustrated in the above results.The match scores for the probe samples are tightly grouped and do notagree. While the left sample in gallery pair A generates the highestmatch score, gallery sample B yields the highest match score for theright sample. Therefore, it may be useful to perform additional analysisto ascertain which set is more likely the genuine match. The analysisbelow illustrates the gallery sample set candidate B exhibits the lowestlikelihood of a false match, several times smaller than that of theother candidates and could be more confidently selected as the correctmatch.

For example, this exemplary analysis technique utilizes the biometricidentification and verification system 100 in a multi-sampleenvironment. Specifically, the match score module 110 determines a firstmatch score between a first probe sample, e.g., a right sample, and afirst gallery sample (right) in a gallery. The module then determines aplurality of gallery match scores between the first gallery sample andthe plurality of other samples in the gallery and determines the numberof the plurality of gallery match scores that are greater than thedetermined first match score. A plurality of probe match scores are thendetermined between the first probe sample and the plurality of othersamples in the gallery as well as the number of the plurality of probematch scores that are greater than the determined first match score.

The match score module 110 then determines a second match score of asecond probe sample, e.g., a left sample, and a second gallery sample ina gallery. The plurality of gallery match scores between the secondgallery sample and the plurality of other samples in the gallery is thendetermined as well as the number of the plurality of gallery matchscores that are greater than the determined second match score. Aplurality of probe match scores between the second probe sample and theplurality of other samples in the gallery is then determined as well asthe number of the plurality of probe match scores that are greater thanthe determined second match score. The match score module 110, incooperation with one or more of the I/O controller 150, processor 160,and memory 170 then combines the determined information from the firstand second samples to determine a false match probability level incooperation with the confidence level module 140.

For example, with a gallery of 10,000 fingerprint/face sample pairs anda probe each including a fingerprint sample and face sample, A, B, C, D,and E represent the genuine candidates with the five highest match scoreresults between probe sample pairs and gallery sample pairs.

In this example, there are 10,000 sample pairs in the gallery. M(P1f:G(X)f) and M(P1 v:G(X)v) represent each match score between respectivefingerprint and face samples. FMO(P1 f:G(X)f) and FMO(P1 v:G(X)v)represent the occurrences of false match scores above the given matchscore of the pair between each probe sample and the entire gallery forfingerprint and face samples, respectively. FMO(G(X)f:G1 f) andFMO(G(X)v:G1 v) represent the occurrences of false match scores abovethe given match score of the pair between each gallery sample and theentire gallery for fingerprint and face samples, respectively. FMO(P1f:G1 f) and FMO(P1 v:G1 v) represent the sum of these false matchoccurrences observed with the probe sample and gallery sample.FMP(P1:G1) represents the probability of a false match result of thegiven probe/gallery set.

Note that regarding probability and calculation of FMP:

P(A and B)=n(A and B)/n(S) where n(S) is the total number of possibleoutcomes

P(A or B)=P(A)+P(B)−P(A and B)

TABLE 3 results of 1: many match of a two-sample multimodal probe andgallery of 10,000 sample sets G(A) G(B) G(C) G(D) G(E) M(f) match scorefingerprint 95 90 85 80 75 M(v) match score face 90 99 92 80 94 FMO(P1f)false match fingerprint 0 1 2 3 5 occurrences FMO(P1v) false match face4 0 3 6 1 occurrences FMO(Gxf) false match fingerprint 8 4 16 31 25occurrences FMO(Gxv) false match face 36 8 14 27 19 occurrences FMP(f)false match fingerprint 0.08% 0.05% 0.18% 0.34% 0.30% occurrences sumFMP(v) false match face 0.40% 0.08% 0.17% 0.33% 0.20% occurrences sumFMP False match 0.4797% 0.1300% 0.3497% 0.6689% 0.4994% probability

The problem with match score is illustrated in the results above; thematch scores for the probe samples are tightly grouped and do not agree.While the fingerprint sample in gallery pair A generates the highestmatch score, gallery sample B yields the highest match score for theface sample. Therefore, it is useful to perform additional analysis toascertain which set is most likely the genuine match.

The analysis illustrates that gallery sample set candidate B exhibit thelowest likelihood of a false match, several times smaller than that ofthe other candidates and can be more confidently selected as the correctmatch.

This exemplary embodiment applied to a multimodal environment includes asimilar series of steps. More specifically, the match score module 110determines a first match score of a first probe sample corresponding toa mode, e.g., a fingerprint, and a first gallery sample in a gallery.The module then determines a plurality of gallery match scores betweenthe first gallery sample and the plurality of other samples in thegallery and determines the number of the plurality of gallery matchscores that are greater than the determined first match score.

The match score module 110 then determines a plurality of probe matchscores between the first probe sample and the plurality of other samplesin the gallery and determines the number of the plurality of probe matchscores that are greater than the determined first match score. A secondmatch score of a second probe sample is corresponding to a mode (e.g., aface and a second gallery sample in the gallery is then determined). Theplurality of gallery match scores between the second gallery sample andthe plurality of other samples in the gallery is then determined as wellas the number of the plurality of gallery match scores that are greaterthan the determined second match score. A plurality of probe matchscores between the second probe sample and the plurality of othersamples in the gallery is then determined as well as the number of theplurality of probe match scores that are greater than the determinedsecond match score. The confidence level module then combines thedetermined information from the first and second sample to determine afalse match probability level.

Biometric Verification

There is also problem in biometric systems where matching performance isnot optimized because the same matching threshold is applied to allmatch pairs regardless of their observed behavior and performance.

Consider the scenario of biometric access control system, whereemployees use biometrics to gain entrance into a facility. A typicalbiometric system works by storing a single biometric sample of eachemployee in a biometric database. Each time the employee attempts toaccess the facility, a live biometric sample is generated and thencompared to the sample from that employee that is stored in thedatabase. A biometric matcher is used to measure the similarity betweenthe two samples, and if the similarity score is above a preset matchthreshold, then the samples are determined to be derived from the samesource, and the individual is granted access to the facility. If thesamples are compared and the similarity score is below the threshold,the individual is determined not to match and thus denied access to thefacility.

But consider the individual whose biometric samples generatecomparatively low match scores to both genuine and impostor samples.This individual, while less likely to cause a false match with animpostor, will experience a higher rate of false non-match incidents.The biometric system would achieve higher overall performance if a lowermatch threshold derived specifically for this individual was appliedupon their access attempts.

Also consider the individual whose biometric samples generatecomparatively high match scores to both genuine and impostor samples.This individual will experience a lower rate of false non-matchincidents, but is more likely to cause a false match with an impostor.The biometric system would achieve higher overall performance if ahigher match threshold derived specifically for this individual wasapplied upon their access attempts.

In accordance with an exemplary embodiment, the biometric identificationand verification system uses previous genuine and impostor match scoredata to automate derivation of an individualized verification matchthreshold score.

More specifically, the following exemplary procedure describes how thisproblem can be addressed using impostor and genuine match score data.

For each individual enrolled in a biometric system, the match scoremodule 110 creates a database of match scores observed between allgenuine and all impostor samples and stores it in a gallery, such asgallery 194. That is, a unique sample pair for all possible combinationsof genuine sample and impostor sample match scores is created.

Next, the histogram module 120 creates a cumulative histogram data tablerecording the number of these impostor match scores that are greaterthan that score divided by the total number of samples. This value isthe “false match rate” associated with each possible threshold used inthe system.

For each individual enrolled in a biometric system, the match scoremodule 110 generates a database of match scores observed between allgenuine samples. That is, a unique sample pair for all possiblecombinations of genuine samples is created.

Then, the histogram module 120 creates a cumulative histogram data tablerecording for each possible match score less than that score divided bythe total number of samples. This value is the “false non-match rate”associated with each possible threshold used in the system.

The threshold module 130 manually or automatically utilizes the matchthreshold that results in FMR and FNMR matching performance within atargeted range.

EXAMPLE

A company maintains a biometric system used for restricting access to afacility. There are 100 employees who have each used the system for 1000days. A single biometric sample is collected from each employee once perday. Therefore, each employee has 1000 genuine samples in the database,and there are a total of 100,000 samples in the database.

For each employee, a “FMR” database is made up of all match scoresgenerated from all unique genuine-impostor pairs. It follows that thisFMR database for each employee contains 1,000*99,000=99,000,000 matchscores.

An FMR histogram is generated for each employee, which is used togenerate a table that relates the associated false match rate to eachmatch score threshold.

Also, for each employee, an “FNMR” database is made up of match scoresgenerated from all unique genuine-genuine pairs. It follows that theFNMR database for each employee contains n(n−1)/2 or1000*(999)/2=499,500 match scores.

An FNMR histogram is generated for each employee, which is used togenerate a table that relates the associated false non-match rate toeach match score threshold.

For each employee, these tables are combined to exhibit both theprobability of a false match and probability of a false non-match foreach threshold value. With this data, a user, with the cooperation ofthe input device 240, can either manually or automatically select amatch threshold that satisfactorily achieves the FMR and FNMR targets ofthe biometric system.

FIG. 16 illustrates an example of a comparison of the number of scoresto match scores. More specifically, highlighted are the low-match scoreindividual; imposter scores, the high-match score individual; imposterscores, the low-match score individual; genuine scores, and thehigh-match score individual; genuine scores.

In FIG. 17, an example is given that illustrates that a lower thresholdoptimizes the FMR and FNMR. Another example is given in FIG. 18, whereit can be seen that a higher threshold optimizes the FMR and FNMR.

FIG. 5 illustrates an exemplary method of using imposter match scoredata to automate derivation of a match threshold score for biometricidentification that results in the desired system false match rate andfalse non-match rate. In particular, control begins in step S500 andcontinues to step S510. In step S510, a database of match scores betweenall unique imposter sample pairs in a gallery is generated. Impostersample pairs are derived from two samples, each from a different source.Next, in step S520, a cumulative histogram data table recording for eachpossible match score outcome the number of match scores observed greaterthan that valued by the total number of samples is created. This valueis the false match rate associated with each possible threshold used inthe system. Then, in step S530, a user selects a desired false matchrate and applies the threshold that will result in that desired falsematch rate. Control then continues to step S540.

In step S540, a user specifies a desired false match rate. Thisthreshold is then applied to the system in step S550 with an optionalalert being sent in step S560 to alert the user to the resulting FMRwith the threshold in use based on actual results using the gallerysamples in use. Control then continues to step S570 where the controlsequence ends.

FIG. 7 illustrates an exemplary method for using genuine match scoredata to automate derivation of a match threshold score that results inthe desired system false match rate and false non-match rate forbiometric identification. In particular, control begins in step S700 andcontinues to step S710. In step S710, a database of match scores betweenall unique genuine sample pairs in a gallery is generated. The genuinesample pairs are derived from two samples, each from the same source.Next, in step S720, a cumulative histogram data table recording for eachpossible match score outcome and number of match scores observed greaterthan that value divided by the total number of samples is created. Thisvalue is the false non-match rate associated with each possiblethreshold used in the system. Then, in step S730, a user can select adesired false non-match rate and apply the threshold that will result inthe desired false non-match rate. Control then continues to step S740.

In step S740, a user can then specify how they would like the system toperform with a false non-match rate of, for example, 1 in 1,000. As withthe previous example, in step S750 the user can optionally be alertedwith control continuing to step S760 where the control sequence ends.

FIG. 9 illustrates an exemplary method of determining a false matchconfidence score according to this invention. In particular, controlbegins in step S900 and continues to step S910. In step S910, a firstmatch score between a probe sample and a gallery sample in a gallery isdetermined. Next, in step S920, a plurality of gallery match scoresbetween the gallery sample and the plurality of other samples in thegallery are determined as well as the number of the plurality of gallerymatch scores that are greater than the determined first match score.Then, in step S930, a plurality of probe match scores between the probesample and the plurality of other samples in the gallery is determinedas well as the number of the plurality of probe match scores that aregreater than the determined first match score. Control then continues tostep S940 where the control sequence ends.

FIG. 10 illustrates an exemplary method of applying the techniquedisclosed herein to a multi-sample environment. In particular, controlbegins in step S1000 and continues to step S1010. In step S1010, a firstmatch score of a first probe sample and a first gallery sample in agallery are determined. Next, in step S1020, a plurality of gallerymatch scores between the first gallery sample and the plurality of othersamples in the gallery are determined as well as the number of theplurality of gallery match scores that are greater than the determinedfirst match score. Then, in step S1030, a plurality of probe matchscores between the first probe sample and the plurality of other samplesin the gallery is determined as well as the number of the plurality ofprobe match scores that are greater than the determined first matchscore. Control then continues to step S1040.

In step S1040, a second match score of a second probe sample and asecond gallery sample in a gallery are determined. Next, in step S1050,a plurality of gallery match scores between the second gallery sampleand the plurality of other samples in the gallery are determined as wellas the number of the plurality of gallery match scores that are greaterthan the determined second match score. Next, in step S1060, a pluralityof probe match scores between the second probe sample and the pluralityof other samples in the gallery is determined as well as the number ofthe plurality of probe match scores that are greater than the determinedsecond match score. In step S1070, the determined information from thefirst and second sample is combined to determine a false matchconfidence level with control continuing to step S1080 where the controlsequence ends.

FIG. 11 illustrates an exemplary embodiment of the extension of thetechnique to a multi-modal environment. In particular, control begins instep S1110 and continues to step S1120. In step S1120, a first matchscore of a first probe sample corresponding to a mode and a firstgallery sample in a gallery are determined. Next, in step S1130, aplurality of gallery match scores between the first gallery sample andthe plurality of other samples in the gallery are determined as well asthe number of plurality of gallery match scores that are greater thanthe determined first match score. Then, in step S1140, a plurality ofprobe match scores between the first probed sample and the plurality ofother samples in the gallery are determined as well as the number of theplurality of probe match scores that are greater than the determinedfirst match score. Control then continues to step S1150.

In step S1150, a second match score of a second probe samplecorresponding to a mode and a second gallery sample in a gallery aredetermined. Next, in step S1160, a plurality of gallery match scoresbetween the second gallery sample and the plurality of other samples inthe gallery are determined as well as the number of plurality of gallerymatch scores that are greater than the determined second match score.Next, in step S1170, a plurality of probe match scores between thesecond probe sample and the plurality of other samples in the galleryare determined as well as the number of the plurality of probe matchscores that are greater than the determined second match score. Thedetermined information from the first and second sample is then combinedto determine a false match confidence level in step S1180 with controlcontinuing to step S1190 where the control sequence ends.

FIG. 15 illustrates an exemplary embodiment for using previous genuineimposter match score data to automate derivation of an individualizedverification match threshold score. In particular, control begins instep S1500 and continues to step S1510. In step S1510, for eachindividual enrolled in a biometric system, a database of match scoresobserved between all genuine and all imposter samples is generated.Next, in step S1520, a cumulative histogram data table recording thenumber of these imposter match scores that are greater than that scoredivided by the total number of samples is created. This value is thefalse match rate associated with each possible threshold used in thesystem. Then, in step S1530, for each individual enrolled in thebiometric system, a database and match score is observed between allgenuine samples as generated. That is, a unique sample pair for allpossible combinations of genuine samples is created. Control thencontinues to step S1540.

In step S1540, a cumulative histogram data table recording for eachpossible match score less than that score divided by the total number ofsamples is created. This value is the false non-match rate associatedwith each possible threshold used in the system. Next, in step S1550,this match threshold that results in FMR and FNMR matching performancewithin a targeted range is one or more of manually and/or automaticallyutilized. Control then continues to step S1560 where the controlsequence ends.

The described systems and methods can be implemented on an imageprocessing device, biometric processing device, fingerprint processingdevice, or the like, or on a separate programmed general purposecomputer having image processing capabilities. Additionally, the systemsand methods of this invention can be implemented on a special purposecomputer, a programmed microprocessor or microcontroller and peripheralintegrated circuit element(s), an ASIC or other integrated circuit, adigital signal processor, a hard-wired electronic or logic circuit suchas discrete element circuit, a programmable logic device such as PLD,PLA, FPGA, PAL, or the like. In general, any device capable ofimplementing a state machine that is in turn capable of implementing theflowcharts illustrated herein can be used to implement the imageprocessing system according to this invention.

Furthermore, the disclosed methods may be readily implemented insoftware stored on a computer-readable media using object orobject-oriented software development environments that provide portablesource code that can be used on a variety of computer or workstationplatforms. Alternatively, the disclosed system may be implementedpartially or fully in hardware using standard logic circuits or a VLSIdesign. Whether software or hardware is used to implement the systems inaccordance with this invention is dependent on the speed and/orefficiency requirements of the system, the particular function, and theparticular software or hardware systems or microprocessor ormicrocomputer systems being utilized. The systems and methodsillustrated herein however can be readily implemented in hardware and/orsoftware using any known or later developed systems or structures,devices and/or software by those of ordinary skill in the applicable artfrom the functional description provided herein and with a general basicknowledge of the computer and image processing arts.

Moreover, the disclosed methods may be readily implemented in softwareexecuted on programmed general purpose computer, a special purposecomputer, a microprocessor, or the like. In these instances, the systemsand methods of this invention can be implemented as program embedded onpersonal computer such as JAVA® or CGI script, as a resource residing ona server or graphics workstation, as a routine embedded in a dedicatedfingerprint processing system, as a plug-in, or the like. The system canalso be implemented by physically incorporating the system and methodinto a software and/or hardware system, such as the hardware andsoftware systems of an image processor.

It is, therefore, apparent that there has been provided, in accordancewith the present invention, systems and methods for biometricidentification and verification which may be particularly useful whenused with fingerprints. While this invention has been described inconjunction with a number of embodiments, it is evident that manyalternatives, modifications and variations would be or are apparent tothose of ordinary skill in the applicable arts. Accordingly, it isintended to embrace all such alternatives, modifications, equivalentsand variations that are within the spirit and scope of this invention.

1. A method for setting a threshold in a biometric system using impostermatch score data to derive a match threshold score that results in adesired false match rate and false non-match rate comprising: creating adatabase of match scores between all unique impostor biometric samplepairs; creating a cumulative histogram data table recording for eachpossible match score outcome a number of match scores observed greaterthan that value divided by the total number of samples; and selecting adesired false match rate and applying the threshold that will result inthe desired false match rate. 2.-60. (canceled)